The Science Behind App Uninstalls Prediction – Part 1/2

app uninstall

Uninstalling an app from our smartphone is the easiest thing to do, right? Enough blogs have mentioned about why customers uninstall apps. A multitude of companies have done surveys and reporting to reach at 10 worst reasons causing uninstalls. For e.g. Google App Marketing Survey says an average app user has 36 apps installed on his/her smartphone. But they use only one-fourth of these apps daily. The remaining one-fourth that are never used are the ones likely to be uninstalled. Placing few other links in the references section.

predictive analytics for appsA Kantar/ITR study from showed that an average of 26% of app installs are uninstalled in the first hour. That uninstall rate rises to 38% in the first day, 64% in the first month, and about 89% over 12 months. Those figures represent the average across all app types.

App Marketer’s KPI woes

If you are a fellow app marketer or a product manager like me, you can feel the pain of a leaky bucket of users uninstalling your app mercilessly. We toil day and night to ensure a sprint app development process, user-friendly app features and carve the simplest user journeys. We then market it just enough to make our app stay on top of Google Play Store rankings. All seems well and we are right on track to achieve that glorious milestone of 1 million app downloads. But our app analytics tools like Google Analytics, Firebase, Apsalar have a completely different story for us.

The report suggests that app uninstalls are on the rise this month, and your (well, mine too) KPIs for “number of active installs” indicates a churn rate of more than 40%.

What’s the Next Plan of Action?

Armed with the intelligence of the analytics tools, we decode the behavioural attributes of the users/ user segments who have uninstalled your app. The reasons behind most of the uninstalls become our next app optimization and development tasks. But we almost forgot about the churned users? How do we win them back?

The first instinct is to take resort in our run-of-the-mill marketing activities.

  1. We run mailer campaigns with offers/marketing gimmicks if they had logged in with their email ids
  2. We send promotional SMS to user inbox if we had managed to capture their phone numbers.

And say, a majority of these users never logged in or gave their personal details to us. Now, as a consequence, we can never retarget them with personalization and the only thing left for us to do is just wait for these users to reinstall our app.

machine learning

What’s the solution? Do we have a way to stop the users from churning in the first place

Predictive Action to Stop Users from Churning?

Machine Learning Prediction Model is the answer. At Tatvic, our data science team has come up with a prediction model which feeds in scores of users, device and behaviour feature from the analytics tools and Tatvic’s internal Uninstall Library and predicts the probability of users who will uninstall within the next ‘n ‘ days. We call it PredictN Model.

Here is how it functions – in brief – we input the cohort of users who were acquired between any 15 day period along with their attributes. With previously trained data of identified churned users, PredictN Model will point out unique users from the said cohort who have more than 75% probability of uninstalling within 7 days.

  1. Retarget these users at once via Paid channels and Push notifications
  2. Understand the pattern of these unique users as to why they might be leaving your app from a geographic, demographic, device specific and/or behavioural perspective.

How does the model work?

Check this space for knowing more on model attributes and results in our next blog chapter.

A takeaway for reading till the end – heartfelt thanks, with an invitation to attend our free webinar lined up on 20th June 2017 where I will talk to you about our Uninstall Prediction Modelling, use-cases to prevent users from churning and our powerful Uninstall Library. Just go ahead and register! And post your comments on how you take predictive actions for uninstalls in your business.



The Science Behind App Uninstalls Prediction - Part 1/2 by
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Bismayy Mohapatra

Bismayy Mohapatra

Bismayy is a Product Manager at Tatvic. He has developed Badger, an intelligent insights generation engine powered by Machine Learning. He is working with team to develop PredictN, an automated prediction SaaS platform for businesses to boost their marketing RoI. He applies R, Python, Cloud Prediction models to help clients make sense of their digital analytics data. He loves playing and following football.
Bismayy Mohapatra

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